Market Vision and Market Visioning Competence: Impact on Early Performance for Radically New, High‐Tech Products<sup>*</sup>
Bibliographic record
Abstract
Having the “right” market vision (MV) in new product scenarios involving high degrees of uncertainty has been shown to help firms achieve a significant competitive advantage, which can ultimately lead to superior financial results. Despite today's increased rate of radical innovation, and hence the importance of effective vision, relatively little research has been undertaken to improve our understanding of this phenomenon. The exploratory and empirical investigation undertaken herewith responds to this research gap by focusing on MV and its precursor, market visioning competence (MVC), for radically new, high-tech products. MV is a clear and specific mental model/image that organizational members have of a desired and important product-market for a new advanced technology, and MVC is a set of individual and organizational capabilities that enable the linking of advanced technologies to a future market opportunity. Based on samples of high-tech firms involved in early technology developments, the measurement study indicates that five factors comprise MV (i.e., clarity, magnetism, specificity, form, and scope) and that four factors underlie MVC (i.e., networking, idea driving, proactive market orientation, and market learning tools). Structural equation modeling is used to demonstrate that MVC significantly and positively impacts MV and that each of these constructs significantly and positively influences certain aspects of early performance (EP) in new product development. This is the first empirical study to develop a comprehensive set of scales to measure these constructs and then to combine them in a model by which to examine their interrelationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".